I am having a little trouble coming up with a way of analyzing my data. If there is a short answer (i.e., "use logistic regression, dummy") you can just post that and I'll do some digging on my own - I just need to be pointed in the right direction...

My independent variable is a count and my dependent variable is a ratio. Here is the data:

success <- c(322,358,323,277)
total.trials <- c(540,533,507,540)
count = c(23,13,21,39)
ratio <- success/total.trials

IIRC, It's wrong to do a simple linear regression of ratio ~ count... so what method should I utilize here? Thanks for the help.

Okay, so here's some of the code I ran after following gung's advice of employing the use of the GEE:

subject <- c(1, 2, 3, 4)
success <- c(322, 358, 323, 277)
total <- c(540, 533, 507, 540)
count <- c(23, 13, 21, 39)
data <- cbind(success,total)

gee.model <- gee(data ~ count, id = subject, family = 'binomial')


gee S-function, version 4.13 modified 98/01/27 (1998) 

Link:                      Logit 
Variance to Mean Relation: Binomial 
Correlation Structure:     Independent 

gee(formula = data ~ count, id = subject, family = "binomial")

Summary of Residuals:
     Min       1Q   Median       3Q      Max 
  276.6608 310.3817 322.1195 331.3620 357.5969 

               Estimate  Naive S.E.   Naive z  Robust S.E.  Robust z
(Intercept) -0.25516680 0.031437649 -8.116599 0.0134033383 -19.03756
count       -0.01055972 0.001244121 -8.487698 0.0002616798 -40.35360

Estimated Scale Parameter:  0.1066564
Number of Iterations:  1

Working Correlation
[1,]    1

Does this look correct? And, if I am interpreting it correctly, there is a significant effect of count on the proportion.

  • $\begingroup$ Welcome to the site. For count data, you should look into poisson regression. In general, the family of models you need to use is determined by the type of dependent variable you have; A continuous DV usually points you toward linear regression. A binary one, toward logistic regression. A count DV, toward poisson or negative binomial regression. This question, and this one be good place to start, even though they might touch on slightly more complicated cases than the one you are dealing with. $\endgroup$ Commented Jan 17, 2013 at 23:13
  • $\begingroup$ Is the actual ratio bounded in any way? $\endgroup$
    – dimitriy
    Commented Jan 17, 2013 at 23:16
  • $\begingroup$ @AntoineVernet, thank you very much - I'll look into the Poisson and NegBin methods. $\endgroup$
    – 123456
    Commented Jan 17, 2013 at 23:18
  • $\begingroup$ @DimitriyV.Masterov - Yes, apologies I forgot to mention that the ratio is actually a percentage, so bound from 0 to 1. Forgive my misuse of terminology. Thank you. $\endgroup$
    – 123456
    Commented Jan 17, 2013 at 23:19
  • $\begingroup$ Do you mean a proportion? In that case, I believe you can use beta regression. $\endgroup$
    – dimitriy
    Commented Jan 17, 2013 at 23:25

1 Answer 1


You have a binary response. That is the important part of this. The count status of your explanatory variable doesn't matter. As a result, you should be doing some form of logistic regression. The part that makes this more difficult is that your data are clustered within just four participants. That means you need to either use a GLiMeM, or the GEE. This is a subtle decision, but I discuss it at some length here: Difference between generalized linear models & generalized linear mixed models in SPSS. Depending on the options that your software affords you, you may also have to un-group your data, so that you have a (very long) matrix where the response listed in each row is a 1 or a 0.

  • $\begingroup$ Okay, fantastic. I'll look into this. I had a feeling logistic regression needed to be used, but I was unsure how to implement it in this structure. Thank you. $\endgroup$
    – 123456
    Commented Jan 17, 2013 at 23:40

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